Phenotypic information in genomic variant databases enhances clinical care and research: The international standards for cytogenomic arrays consortium experience

Whole‐genome analysis, now including whole‐genome sequencing, is moving rapidly into the clinical setting, leading to detection of human variation on a broader scale than ever before. Interpreting this information will depend on the availability of thorough and accurate phenotype information, and th...

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Published in:Human mutation Vol. 33; no. 5; pp. 787 - 796
Main Authors: Riggs, Erin Rooney, Jackson, Laird, Miller, David T., Van Vooren, Steven
Format: Journal Article
Language:English
Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01-05-2012
Hindawi Limited
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Summary:Whole‐genome analysis, now including whole‐genome sequencing, is moving rapidly into the clinical setting, leading to detection of human variation on a broader scale than ever before. Interpreting this information will depend on the availability of thorough and accurate phenotype information, and the ability to curate, store, and access data on genotype–phenotype relationships. This idea has already been demonstrated within the context of chromosomal microarray (CMA) testing. The International Standards for Cytogenomic Arrays (ISCA) Consortium promotes standardization of variant interpretation for this technology through its initiatives, including the formation of a publicly available database housing clinical CMA data. Recognizing that phenotypic data are essential for the interpretation of genomic variants, the ISCA Consortium has developed tools to facilitate the collection of these data and its deposition in a standardized structured format within the ISCA Consortium database. This rich source of phenotypic data can also be used within broader applications such as developing phenotypic profiles of emerging genomic disorders, identification of candidate regions for particular phenotypes, or creation of tools for use in clinical practice. We summarize the ISCA experience as a model for ongoing efforts incorporating phenotype data with genotype data to improve the quality of research and clinical care in human genetics. Hum Mutat 33:787–796, 2012. © 2012 Wiley Periodicals, Inc.
Bibliography:National Institutes of Health - No. #HD064525
istex:143386E8BDB025AF4A5CCC3F47B459F820AF1C56
For the Deep Phenotyping Special Issue
ArticleID:HUMU22052
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ISSN:1059-7794
1098-1004
DOI:10.1002/humu.22052